Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network

Rosales, Marife A.; Palconit, Maria Gemel B.; Vicerra, Ryan Rhay P.; Dadios, Elmer P.; Calinao, Hilario A.

doi:10.1109/tencon50793.2020.9293804

Cited by 7 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During neural network training, several hidden neurons were utilised and compared, and it was discovered that using 10 neurons provided the best Pearson’s correlation value and lowest mean square error. According to the results, the SCG algorithm performs better with 10 neurons [ 23 ]. Developers and researchers of the twenty-first century have a considerable focus on harvesting solar energy in an optimal way.…”

Section: Related Workmentioning

confidence: 99%

A Web-Based Model to Predict a Neurological Disorder Using ANN

et al. 2022

View full text Add to dashboard Cite

Dementia is a condition in which cognitive ability deteriorates beyond what can be anticipated with natural ageing. Characteristically it is recurring and deteriorates gradually with time affecting a person’s ability to remember, think logically, to move about, to learn, and to speak just to name a few. A decline in a person’s ability to control emotions or to be social can result in demotivation which can severely affect the brain’s ability to perform optimally. One of the main causes of reliance and disability among older people worldwide is dementia. Often it is misunderstood which results in people not accepting it causing a delay in treatment. In this research, the data imputation process, and an artificial neural network (ANN), will be established to predict the impact of dementia. based on the considered dataset. The scaled conjugate gradient algorithm (SCG) is employed as a training algorithm. Cross-entropy error rates are so minimal, showing an accuracy of 95%, 85.7% and 89.3% for training, validation, and test. The area under receiver operating characteristic (ROC) curve (AUC) is generated for all phases. A Web-based interface is built to get the values and make predictions.

show abstract

Section: Related Workmentioning

confidence: 99%

A Web-Based Model to Predict a Neurological Disorder Using ANN

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Solutions in computer networks and 23 [17], [40], [49], [50], [51], [63], [64], [65], [68], security [70], [72], [80], [93], [94], [99], [101], [103], [104], [105], [106], [113], [120], [122],…”

Section: IIImentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Shukla¹

2022

IJSREM

View full text Add to dashboard Cite

Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2021 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Keywords: Research methods in machine learning, machine learning algorithms, machine learning techniques.

show abstract

“…An artificial neural network is a supervised classification technique replicating biological neurons' connection structure [12]. It comprises many interconnected neurons working together to solve problems [13].…”

Section: Introductionmentioning

confidence: 99%

Classification of Electromyography Signal of Diabetes using Artificial Neural Networks

Zulkifli¹,

Nasir²

2022

IJACSA

View full text Add to dashboard Cite

Diabetes is one of the most chronic diseases, with an increasing number of sufferers yearly. It can lead to several serious complications, including diabetic peripheral neuropathy (DPN). DPN must be recognized early to receive appropriate treatment and prevent disease exacerbation. However, due to the rapid development of machine learning classification, like in the health science sector, it is very easy to identify DPN in the early stages. Therefore, the aim of this study is to develop a new method for detecting neuropathy based on the myoelectric signal among diabetes patients at a low cost with utilizing one of the machine learning techniques, the artificial neural network (ANN). To that aim, muscle sensor V3 is used to record the activity of the anterior tibialis muscle. Then, the representative time domain features which is mean absolute value (MAV), root mean square (RMS), variance (VAR), and standard deviation (SD) used to evaluate fatigue. During neural network training, a different number of hidden neurons were used, and it was found that using seven hidden neurons showed a high accuracy of 98.6%. Thus, this work indicates the potential of a low-cost system for classifying healthy and diabetic individuals using an ANN algorithm.

show abstract

Prediction of Total Body Water using Scaled Conjugate Gradient Artificial Neural Network

Cited by 7 publications

References 10 publications

A Web-Based Model to Predict a Neurological Disorder Using ANN

A Web-Based Model to Predict a Neurological Disorder Using ANN

Research Methods in Machine Learning: A Content Analysis

Classification of Electromyography Signal of Diabetes using Artificial Neural Networks

Contact Info

Product

Resources

About